Synchronization And Control Of Several Types Of Complex Networks Based On Network Characteristics And Time Lag

Recently,synchronization,the ubiquitous and important collective behaviors in complex networks,existing widely in the nature and the production and living of human society,has aroused an increasing wide concern among researchers from different fields,and the investigation of network’s synchronization involves many kinds of interdiscipline,such as differential dynamic system,matrix theory,modern network science control theory,etc..There are several synchronous types,among those the most representative ones are complete synchronization and cluster synchronization.Therefore,it is realistic and valuable in theoretically to focus on synchronization of networks for understanding types of organization in nature and constructing the physical networks which are convenient to production and people’s living.Additionally,with the development of network technology,there are increasing demand on real time control in networked systems from various fields,including industry,military,robot and aerospace.As a result,it is more realistic naturally to study the control in networked systems with respect to the wide application in engineering about the control based on networks.This degree thesis mainly considers the complete synchronization and cluster synchronization in mixed time-varying delays complex networks with external perturbation,as well as concerning the control issue in networked control systems.The main contents are listed as follows:Firstly,some necessary definitions and lemmas are introduced as the mainly support for late analysis.Next,we make some efforts on the synchronization analysis of nonlinearly hybridcoupled delayed dynamical networks with switching topologies under stochastic disturb.By using single impulsive pinning control,as well as considering the impulsive effect between nodes signal exchange,with introducing an improved impulsive differential inequality and the average impulsive interval,some sufficient conditions to achieve complete synchronization are obtained based on nonlinear theory,Lyapunov stable theory and comparison principle.Then,by using a certain neural network system representing nodes’ dynamical behavior as the example,which contains 6 nodes and the topologies are switched between two models,the results of numerical experimentation show correctness of criteria that obtained from the theory result.Then,we discuss the problem about the cluster synchronization for nonlinearly timevarying delayed coupling complex networks with stochastic perturbation.Introducing the periodically intermittent pinning control,without loss of generality,during the work time,we add the controller into the first one node in each cluster;while no nodes are controlled during the rest time.By using Lyapunov stable analysis and stochastic theory,the sufficient criteria that depends on clusters to achieve cluster synchronization are derived.Then,by using the neural systems contains 18 nodes and 3 clusters as the example,the correctness of theory criteria is verified as a result from numerical experimentation.Finally,the control problem about uncertain multi-plant networked systems with external perturbation is taken into consideration.By a reference model and learning from the thought about parameter projection law,we design an adaptive update law for parameters update in the networked system,by which we can estimate and regulate the parameter with real time online.And by using adaptive model-based event-triggered strategy,the event-triggered laws are obtained to guarantee the stable performance of networked systems.At the same time,the lower bound of the internal-execution time is also estimated.In addition,the obtained event-triggered laws can ensure the stable performance of the networked system with external disturbance.In the end,a numerical example of networked control system contains 6 plants is introduced and outcome of the experiment shows the validity of the main theoretical result.